基于弱监督小波KAN网络的弱标注辐射源识别算法  

Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network

作　　者：刘康晟 凌青 闫文君 张立民 于柯远 刘恒燕 LIU Kangsheng;LING Qing;YAN Wenjun;ZHANG Limin;YU Keyuan;LIU Hengyan(Institute of Information Fusion,Naval Aviation University,Yantai 264001,China)

机构地区：[1]海军航空大学信息融合研究所,烟台264001

出　　处：《雷达学报(中英文)》2025年第2期338-352,共15页Journal of Radars

基　　金：国家自然科学基金(62371465);泰山学者工程专项经费基金(ts201511020);山东省青创团队资助(2022KJ084)。

摘　　要：当前辐射源个体识别技术多数基于有监督学习条件下开展,不适应由于采集环境(天气条件、地形和障碍物、干扰源)、器件性能(雷达分辨率、信号处理能力、硬件故障)、标注者水平等因素导致的大范围标签缺失的情形。该文提出了一种基于弱监督小波KAN(WSW-KAN)网络的弱标注辐射源识别算法。该算法首先结合KAN网络独有的边缘函数可学习特性和小波函数的多分辨率分析特性,构建WSW-KAN基线网络;然后将弱标注数据集拆分为小样本有标注数据集和大样本无标注数据集,利用小样本有标注数据集初步训练模型;最后在预训练模型基础上,基于自适应感知伪标签加权选择方法(APLWS),采用对比学习方法提取无标签数据特征并迭代训练,从而有效提高模型的泛化能力。基于真实采集雷达数据集验证,该文所提出的算法对特定辐射源个体识别精度达到95%左右,且算法效率高、参数规模小、适应能力强,能够满足实际场景的需求。Most existing specific emitter identification technologies rely on supervised learning,making them unsuitable for scenarios with label loss due to factors such as the acquisition environment(e.g.,weather conditions,terrain,obstacles,and interference sources),device performance(e.g.,radar resolution,signal processing capabilities,and hardware failures),and tagger level.In this study,a weakly labeled specific emitter identification algorithm based on the Weakly Supervised Wav-KAN(WSW-KAN)network is proposed.First,a WSW-KAN baseline network is constructed by integrating the unique learnable edge function of the KAN network with the multiresolution analysis of the wavelet function.The weakly labeled dataset is then divided into a small labeled dataset and a large unlabeled dataset,with the small labeled dataset used for initial model training.Finally,based on the pretrained model,Adaptive Pseudo-Label Weighted Selection(APLWS)is used to extract features from the unlabeled data using a contrast learning method,followed by iterative training,thereby effectively improving the generalization capability of the model.Experimental validation using a real acquisition radar dataset demonstrates that the proposed algorithm achieves a recognition accuracy of approximately 95%for specific emitters while maintaining high efficiency,a small parameter scale,and strong adaptability,making it suitable for practical applications.

关 键 词：辐射源个体识别 弱监督小波KAN 伪标签迭代 弱监督学习 对比学习 神经网络 

分 类 号：TN911.7[电子电信—通信与信息系统]